The subject matter disclosed herein generally relates to generating semantic annotations. More specifically, the subject matter relates to systems and methods for generating an event narrative, i.e., a sequence of semantic annotations for an event based on semantic annotations of previous events.
Current intelligence platforms receive vast amounts of inputs (e.g., video inputs, audio inputs, geographic signals, and the like) and determine critical events from the received inputs, using rule based systems, finite state machines, and the like. For example, a manufacturing factory includes a video surveillance system for receiving a live-video feed and detects a crime scene from the received video-feed. In such an example, the video surveillance system generates metadata from the live-video feed and analyzes whether the metadata satisfies, for example, a pre-defined rule describing a crime.
Such intelligence platforms have numerous problems. For example, such intelligence platforms fail to detect events that have not been previously encountered and pre-defined by an administrator of the intelligence platform. Thus, there is a need for an enhanced system and method for generating semantic annotations which will allow for reasoning over events that transcends the analysis of raw meta-data.